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English(EN) Gone for Good? I asked 10 models which deleted data could actually be restored

Gemini 3.7 Flash 在预测数据丢失和恢复方面引领LLM基准测试

Kaggle上的一个基准测试挑战,测试了十个大型语言模型在执行破坏性文件系统和数据库命令后预测数据丢失和恢复的能力。Gemini 3.7 Flash 在18对命令中实现了完美的准确性,正确识别了所有丢失和可恢复的数据。相比之下,包括GPT-5.4和Gemini 3.1 Flash-Lite在内的几个模型则表现不佳,常常无法区分数据丢失和成功恢复,有时对同一命令的变体给出相同的错误答案。 AI

影响 此基准测试突显了LLM准确理解文件系统和数据库操作对于可靠的编码辅助和数据管理至关重要。

排序理由 该项目描述了一个评估LLM在特定技术任务上表现的基准测试挑战,类似于学术研究。[lever_c_demoted from research: ic=1 ai=1.0]

在 dev.to — LLM tag 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

Gemini 3.7 Flash 在预测数据丢失和恢复方面引领LLM基准测试

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该项目描述了一个评估LLM在特定技术任务上表现的基准测试挑战,类似于学术研究。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Syed Jawad ·

    永不消失?我询问了10个模型,哪些已删除数据实际上可以恢复

    <p><em>This is a submission for the <a href="https://dev.to/challenges/kaggle-2026-09-23">Kaggle Benchmarking Challenge</a></em></p> <blockquote> <p><strong>TL;DR.</strong> I gave 10 models 18 matched pairs of destructive filesystem, git and SQLite commands and asked two things: …